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Entropy based feature extraction for motorbike engine faults diagnosing using neural network and wavelet transform

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3 Author(s)
M. P. Paulraj ; School of Mechatronics Engineering, Universiti Malaysia Perlis (UniMAP), Malaysia ; Sazali Yaacob ; Mohd Zubir Md Zin

The sound of working vehicle provides an important clue for engine faults diagnosis. Endless efforts have been put into the research of fault diagnosis based on sound. It offers concrete economic benefits, which can lead to high system reliability and save maintenance cost. A number of diagnostic systems for vehicle repair have been developing in recent years. Artificial neural network is a very demanding application and popularly implemented in many industries including condition monitoring via fault diagnosis. This paper presents a feature extraction algorithm using total entropy of 5 level decomposition of wavelet transform. The engine noise signal is decomposed into 5 levels (A5, D5, A4, D4, A3, D3, A2, D2, A1, D1) using Daubechies "db4" wavelet family. From the decomposed signals, the entropy is applied for each levels and the feature are extracted and used to develop a backpropagation neural network.

Published in:

Signal Processing & Its Applications, 2009. CSPA 2009. 5th International Colloquium on

Date of Conference:

6-8 March 2009